# Linear regression in numpy and scipy

Note that there are multiple numpy/scipy functions that do regression, fitting, etc. Of these, `scipy.stats.linregress`

fits a line and forces an intercept. You do not have to explicitly add 1s or anything. `numpy.linalg.lstsq`

does plain old linear regression - your inputs can even be matrices. It simply returns `argmin |ax - b|^2`

for given `a`

and `b`

, and therefore does not force an intercept. The last one I want to mention is `scipy.optimize.leastsq`

. This one is a non-linear least squares solver, and I know nothing more about it.

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